Explore the vast range of titles available.

Energy plays a crucial role in driving economic growth, and India’s energy consumption has increased notably due to its growing population and development. At present, fossil fuels such as coal, petroleum, and natural gas fulfill the majority of India’s energy requirements, but their swift depletion and negative environmental effects present significant challenges. India’s abundant solar energy potential—estimated at approximately 5000 trillion kWh annually—positions the nation to harness clean and sustainable power. With steady growth, solar energy has become a key component of India’s power grid. However, integrating renewable energy into the grid presents challenges, such as maintaining frequency and voltage stability. This report analyzes India’s substantial advancements in solar energy, emphasizing the enabling government policies and the problems associated with integrating renewable energy into the grid. The study underscores the crucial need for effective load frequency control (LFC) solutions to mitigate grid stability issues, intensified by the fluctuating and intermittent characteristics of solar energy. It also evaluates policy-driven approaches and technological advancements, providing practical recommendations to overcome integration challenges. This research aims to contribute to the effective deployment of solar energy in India’s energy mix, ensuring long-term grid stability and sustainability, and it underscores that India’s creative strategies can serve as a model for other nations facing analogous issues in renewable energy integration. It emphasizes the necessity of recognizing optimal practices that integrate energy security, economic development, and environmental objectives, thus contributing to global dialogs on energy transitions.

The purpose of this research is to present an innovative load frequency control in the presence of wind turbines in islanded micro-grid (MG). As islanded MG suffers from low inertia and insufficient primary frequency response (PFR), utilising the variable wind turbines in de-loaded area can be considered as an alternative solution to deal with frequency control problems. In this context, the de-load area is referred to a region where wind turbines release their stored kinetic energy in rotational masses following frequency disturbances. For effective utilisation of wind turbines, a self-tuning, adaptive fuzzy droop is proposed, whose membership function parameters are optimised through artificial bee colony algorithm based on a multi-objective decision making process. A comparison is made between the obtained results of the self-tuning, adaptive fuzzy droop with conventional proportional integral derivative droop control in order to assess the proposed method performance in different disturbances.

This study presents an innovative strategy for load frequency control (LFC) using a combination structure of tilt-derivative and tilt-integral gains to form a TD-TI controller. Furthermore, a new improved optimization technique, namely the quantum chaos game optimizer (QCGO) is applied to tune the gains of the proposed combination TD-TI controller in two-area interconnected hybrid power systems, while the effectiveness of the proposed QCGO is validated via a comparison of its performance with the traditional CGO and other optimizers when considering 23 bench functions. Correspondingly, the effectiveness of the proposed controller is validated by comparing its performance with other controllers, such as the proportional-integral-derivative (PID) controller based on different optimizers, the tilt-integral-derivative (TID) controller based on a CGO algorithm, and the TID controller based on a QCGO algorithm, where the effectiveness of the proposed TD-TI controller based on the QCGO algorithm is ensured using different load patterns (i.e., step load perturbation (SLP), series SLP, and random load variation (RLV)). Furthermore, the challenges of renewable energy penetration and communication time delay are considered to test the robustness of the proposed controller in achieving more system stability. In addition, the integration of electric vehicles as dispersed energy storage units in both areas has been considered to test their effectiveness in achieving power grid stability. The simulation results elucidate that the proposed TD-TI controller based on the QCGO controller can achieve more system stability under the different aforementioned challenges.

Since modern power systems are susceptible to undesirable frequency oscillations caused by uncertainties in renewable energy sources (RESs) and loads, load frequency control (LFC) has a crucial role to get these systems’ frequency stability back. However, existing LFC techniques may not be sufficient to confront the key challenge arising from the low-inertia issue, which is due to the integration of high-penetration RESs. Therefore, to address this issue, this study proposes an optimized intelligent fractional-order integral (iFOI) controller for the LFC of a two-area interconnected modern power system with the implementation of virtual inertia control (VIC). Here, the proposed iFOI controller is optimally designed using an efficient metaheuristic optimization technique, called the gray wolf optimization (GWO) algorithm, which provides minimum values for system frequency deviations and tie-line power deviation. Moreover, the effectiveness of the proposed optimal iFOI controller is confirmed by contrasting its performance with other control techniques utilized in the literature, such as the integral controller and FOI controller, which are also designed in this study, under load/RES fluctuations. Compared to these control techniques from the literature for several scenarios, the simulation results produced by the MATLAB software have demonstrated the efficacy and resilience of the proposed optimal iFOI controller based on the GWO. Additionally, the effectiveness of the proposed controller design in regulating the frequency of interconnected modern power systems with the application of VIC is confirmed.

The automatic load frequency control for multi-area power systems has been a challenging task for power system engineers. The complexity of this task further increases with the incorporation of multiple sources of power generation. For multi-source power system, this paper presents a new heuristic-based hybrid optimization technique to achieve the objective of automatic load frequency control. In particular, the proposed optimization technique regulates the frequency deviation and the tie-line power in multi-source power system. The proposed optimization technique uses the main features of three different optimization techniques, namely, the Firefly Algorithm (FA), the Particle Swarm Optimization (PSO), and the Gravitational Search Algorithm (GSA). The proposed algorithm was used to tune the parameters of a Proportional Integral Derivative (PID) controller to achieve the automatic load frequency control of the multi-source power system. The integral time absolute error was used as the objective function. Moreover, the controller was also tuned to ensure that the tie-line power and the frequency of the multi-source power system were within the acceptable limits. A two-area power system was designed using MATLAB-Simulink tool, consisting of three types of power sources, viz., thermal power plant, hydro power plant, and gas-turbine power plant. The overall efficacy of the proposed algorithm was tested for two different case studies. In the first case study, both the areas were subjected to a load increment of 0.01 p.u. In the second case, the two areas were subjected to different load increments of 0.03 p.u and 0.02 p.u, respectively. Furthermore, the settling time and the peak overshoot were considered to measure the effect on the frequency deviation and on the tie-line response. For the first case study, the settling times for the frequency deviation in area-1, the frequency deviation in area-2, and the tie-line power flow were 8.5 s, 5.5 s, and 3.0 s, respectively. In comparison, these values were 8.7 s, 6.1 s, and 5.5 s, using PSO; 8.7 s, 7.2 s, and 6.5 s, using FA; and 9.0 s, 8.0 s, and 11.0 s using GSA. Similarly, for case study II, these values were: 5.5 s, 5.6 s, and 5.1 s, using the proposed algorithm; 6.2 s, 6.3 s, and 5.3 s, using PSO; 7.0 s, 6.5 s, and 10.0 s, using FA; and 8.5 s, 7.5 s, and 12.0 s, using GSA. Thus, the proposed algorithm performed better than the other techniques.

In the proposed research article, the grey wolf optimization (GWO) technique is utilized to optimize the proportional (P) integral (I) derivative (D) (PID) controller/regulator gain parameters in three-area grid-connected power networks. The interconnected power plant covers thermal plants, hydro plants, and nuclear power plants. The proposed controller is used as a secondary controller in the power system to perform load frequency control (LFC). Under unforeseen load conditions, the system frequency deviates from the norm. To control and stabilize this oscillation, the LFC system is used. During the investigation, a step load perturbation of one percent (SLP 1%) is applied for the analysis of the thermal power plant. The response of the suggested optimization technique-designed regulator performance is equated with the genetic algorithm (GA)-tuned, particle swarm optimization (PSO)-tuned, and ant colony optimization (ACO) technique-tuned PID regulator response. The performance response is evidence that the GWO-based PID regulator provides a regulated response with minimal time-domain specification parameters (settling time, peak shoots) over other tuning methods. The effectiveness and robustness of the improved response of the suggested technique-optimized controller are verified with various load values (1%, 2%, and 10% SLP) and nominal parameter ( R , T p , and T ij ) variations (± 25% & ± 50%) from its nominal value.

With the increasing participation of flexible loads in power grid regulation, the heterogeneity of flexible loads poses a critical challenge in load frequency control (LFC) of power systems. This paper proposes a novel hybrid model predictive control (HMPC)‐based LFC strategy for power systems with heterogeneous controllable loads (HCLs), comprising continuous‐type controllable loads (CCLs) and discrete‐type controllable loads (DCLs). To the best of our knowledge, this is the first paper to introduce discrete‐type controllable resources into the LFC framework and formulate a hybrid predictive model incorporating both continuous and discrete control inputs. Due to the hybrid predictive model involving both continuous and integer decision variables, we employ a mixed‐integer linear quadratic programing (MILQP) algorithm to solve the problem and obtain the optimal LFC strategy. Simulation results on a three‐area interconnected power system demonstrate that the proposed HMPC‐based LFC achieves improved performance over traditional PI‐based LFC in terms of overshoot (OS) reduction, oscillation damping, and regulation speed. The effectiveness of the proposed approach depends on the accuracy of the LFC model, which may present challenges in scenarios with significant system uncertainties. Nonetheless, this research contributes an innovative control paradigm for LFC design in power systems with heterogeneous regulation resources.

A major problem in power systems is achieving a match between the load demand and generation demand, where security, dependability, and quality are critical factors that need to be provided to power producers. This paper proposes a proportional–integral–derivative (PID) controller that is optimally designed using a novel artificial rabbits algorithm (ARA) for load frequency control (LFC) in multi-area power systems (MAPSs) of two-area non-reheat thermal systems. The PID controller incorporates a filter with such a derivative coefficient to reduce the effects of the accompanied noise. In this regard, single objective function is assessed based on time-domain simulation to minimize the integral time-multiplied absolute error (ITAE). The proposed ARA adjusts the PID settings to their best potential considering three dissimilar test cases with different sets of disturbances, and the results from the designed PID controller based on the ARA are compared with various published techniques, including particle swarm optimization (PSO), differential evolution (DE), JAYA optimizer, and self-adaptive multi-population elitist (SAMPE) JAYA. The comparisons show that the PID controller’s design, which is based on the ARA, handles the load frequency regulation in MAPSs for the ITAE minimizations with significant effectiveness and success where the statistical analysis confirms its superiority. Considering the load change in area 1, the proposed ARA can acquire significant percentage improvements in the ITAE values of 1.949%, 3.455%, 2.077% and 1.949%, respectively, with regard to PSO, DE, JAYA and SAMPE-JAYA. Considering the load change in area 2, the proposed ARA can acquire significant percentage improvements in the ITAE values of 7.587%, 8.038%, 3.322% and 2.066%, respectively, with regard to PSO, DE, JAYA and SAMPE-JAYA. Considering simultaneous load changes in areas 1 and 2, the proposed ARA can acquire significant improvements in the ITAE values of 60.89%, 38.13%, 55.29% and 17.97%, respectively, with regard to PSO, DE, JAYA and SAMPE-JAYA.

The mismatch between generated power and load demand often leads to undesirable fluctuations in the frequency and tie-line power change of a power system. To mitigate this problem, the implementation of a control process known as load frequency control (LFC) becomes essential. The objective of this study is to optimize the parameters of the LFC controller for a two-area power system consisting of a reheat thermal generator and a photovoltaic power plant. A proportional–integral (PI) controller is employed to damp the oscillations that occur in the frequency and tie-line power change. A newly developed meta-heuristic optimization technique called gorilla troops optimization (GTO) is used for the first time to optimally tune the parameters of the PI controller and improve its performance. The performance of the GTO optimization technique is analyzed under varying load demands, parameter variations, and nonlinearities. Comparative evaluations with different optimization algorithms are performed. The obtained results demonstrate that the proposed GTO-PI controller outperforms the other optimization techniques in terms of reducing the overshoot values in the system frequency and tie-line power change, as well as achieving faster settling times for these oscillations. This research highlights the effectiveness of the GTO-PI controller in LFC, providing improved performance over alternative algorithms. The results underscore the significance of utilizing meta-heuristic optimization techniques for optimal parameter tuning in power system control applications.

Automatic Generation Control (AGC) delivers a high quality electrical energy to energy consumers using efficient and intelligent control systems ensuring nominal operating frequency and organized tie-line power deviation. Subsequently, for the AGC analysis of a two-area interconnected hydro-gas-thermal-wind generating unit, a novel Fractional Order Integral-Tilt Derivative (FOI-TD) controller is proposed that is fine-tuned by a powerful meta-heuristic optimization technique referred as Improved-Fitness Dependent Optimizer (I-FDO) algorithm. For more realistic analysis, various constraints, such as Boiler Dynamics (BD), Time Delay (TD), Generation Rate Constraint (GRC), and Governor Dead Zone (GDZ) having non-linear features are incorporated in the specified system model. Moreover, a comparative analysis of I-FDO algorithm is performed with state-of-the-art approaches, such as FDO, teaching learning based optimization, and particle swarm optimization algorithms. Further, the proposed I-FDO tuned controller is compared with Fractional Order Tilt Integral Derivative (FOTID), PID, and Integral-Tilt Derivative (I-TD) controllers. The performance analysis demonstrates that proposed FOI-TD controller provides better performance and show strong robustness by changing system parameters and load condition in the range of  ± 50%, compared to other controllers.